Accurate prior modeling in the locally adaptive window-based wavelet denoising

Abstract

The locally adaptive window-based (LAW) denoising method has been extensively studied in literature for its simplicity and effectiveness. However, our statistical analysis performed on its prior estimation reveals that the prior is not estimated properly. In this paper, a novel maximum likelihood prior modeling method is proposed for better characterization of the local variance distribution. Goodness of fit results shows that our proposed prior estimation method can improve the model accuracy. A modified LAW denoising algorithm is then proposed based on the new prior. Image denoising experimental results demonstrate that the proposed method can significantly improve the performance in terms of both peak signal-to noise ratio (PSNR) and visual quality, while maintain a low computation.